Full TCO of an on-premise GPU cluster: from capex to the all-in €/GPU-hour and the break-even against cloud
Contents
Notation: amounts in N € or N USD (source denominated in dollars); decimal point; comma as thousands separator. The dollar sign is not used (it is the formula delimiter). Data centred on Europe/Spain. Generic example hardware: a cluster of N nodes, each with 4×H100 SXM5 80 GB.
TL;DR
A 4×H100 SXM5 node costs between 150,000 USD and 200,000 USD in total capex (GPUs + server + network + storage + prorated rack). Amortised over 3 years with European opex (energy at ~0.116 €/kWh industrial, PUE 1.54 average or 1.2 with liquid, 0.3 FTE of staff), the all-in cost ranges from 3.10 USD/GPU-hour (100 % utilisation) to 6.20 USD/GPU-hour (50 % utilisation). The break-even against AWS p5 on-demand (~6.88 USD/GPU-hour) is crossed at around 70 % utilisation; against a neocloud 3-year reserved (~1.49–2.10 USD/GPU-hour), on-prem never closes the gap in that scenario. Utilisation is the variable that decides the cost axis, not the price of the hardware.
The model: declared assumptions
Every calculation below starts from these assumptions. Changing any of them moves the conclusion; the sensitivity section quantifies by how much.
| Parameter | Base value | Sensitivity range |
|---|---|---|
| Node | 4×H100 SXM5 80 GB (HGX baseboard) | — |
| Cluster | N nodes (per-node model; scales linearly) | 1–32 nodes |
| Capex amortisation | 3 years (straight line) | 3–5 years |
| GPU utilisation | 70 % | 30 %–100 % |
| Energy price | 0.116 €/kWh (industrial Spain, Sept. 2025) | 0.06–0.20 €/kWh |
| PUE | 1.54 (global average, Uptime Institute 2025) | 1.15–1.80 |
| Exchange rate | 1 USD = 0.93 € (reference Jun. 2026) | — |
Energy source: GlobalPetrolPrices · Spain Business Electricity, Sept. 2025. PUE source: Uptime Institute Global Data Center Survey 2025 — the global average PUE has been stuck at 1.54 for the sixth consecutive year; hyperscalers 1.10–1.15; colocation/enterprise 1.58–1.80; facilities less than 5 years old, 1.48. PUE 1.2 is achievable with direct-to-chip liquid cooling.
Capex breakdown per 4×H100 SXM5 node
GPUs
| Component | Unit price (USD) | Quantity | Subtotal (USD) | Source and date |
|---|---|---|---|---|
| H100 SXM5 80 GB (card) | 30,000–40,000 | 4 | 120,000–160,000 | GMI Cloud, Apr. 2026 · Introl, Apr. 2026 |
The range reflects market variability and volume discounts (5–15 % for orders above 50 units). H100 SXM5 cards require NVIDIA’s HGX baseboard; they are not sold loose for direct installation in standard servers.
Server / HGX baseboard
A complete 4×H100 SXM node uses NVIDIA’s HGX H100 4-GPU baseboard plus a compatible host server. Reference models: Supermicro SYS-421GU-TNXR (4U, dual Intel Xeon 4th Gen, HGX H100 4-GPU) and its Dell equivalent.
| Component | Estimated cost (USD) | Note |
|---|---|---|
| Server chassis + CPU (2× Xeon) + RAM (512 GB DDR5) + redundant PSU | 18,000–25,000 | Based on a Supermicro SYS-821GE bare without GPUs at ~24,806 USD (xicomputer.com, Jun. 2026); scaled to the 4-GPU |
| HGX H100 4-GPU baseboard | included in the GPU price | NVIDIA HGX platform; no separate public price |
| Inter-GPU NVLink (inside the node) | included in the baseboard | 4 GPUs connected by NVLink 4.0 on the HGX baseboard |
Marketing claim (no independent verification): Supermicro announces datacenter energy cost reductions of up to 40 % with liquid cooling in its HGX H100 servers (Supermicro press release).
InfiniBand NDR network
For a multi-node cluster with tensor parallelism across nodes, the GPU-to-GPU network is critical. NDR InfiniBand (400 Gb/s per port) is the de facto standard for HGX clusters.
| Component | Estimated cost per node (USD) | Source / Note |
|---|---|---|
| NVIDIA Quantum-2 NDR 400G switch (64 ports, prorated across N nodes) | 2,000–4,000 | Switch ~35,000 USD (Introl, Apr. 2026); at 16 nodes, ~2,200 USD/node |
| InfiniBand NDR cables/transceivers (4 ports per node × ~1,000 USD/port) | 4,000 | Estimate based on ~1,000 USD per optical transceiver (Introl, Apr. 2026) |
| InfiniBand network (prorated per 4-GPU node) | ~6,000–8,000 | — |
For inference serving inside a single node (4 GPUs with NVLink), the inter-node network is less critical than for multi-node training. For disaggregated prefill-decode workloads spread across nodes, InfiniBand NDR is necessary.
NVMe storage
| Component | Estimated cost (USD) | Note |
|---|---|---|
| Local NVMe (4 TB × 2 U.2/E1.S drives, working datasets and checkpoints) | 2,000–4,000 | ~500–1,000 USD/TB enterprise NVMe, 2025 |
| Shared object storage (NAS/MinIO, prorated per node) | 2,000–5,000 | Varies with the total capacity of the cluster |
| Total storage per node | ~4,000–9,000 | — |
Introl models 50 TB per GPU for effective operation in training clusters (Introl, Apr. 2026); for pure inference the requirement is significantly lower (model weights + logs).
Rack, PDU and datacenter connectivity
| Component | Estimated cost per node (USD/year) | Source |
|---|---|---|
| Rack colocation (high density, 10–15 kW per node) | 5,000–12,000/year | Encoradvisors · Colocation Pricing 2026: high density 3,000–6,000 USD/month per rack; at 2 nodes per rack, ~1,500–3,000 USD/month per node = 18,000–36,000 USD/year in tier-1; lower in Spain |
| Rack PDU, electrical cabling (prorated) | 500–1,000 per node (amortised capex) | Inside the colocation line item or in an own datacenter |
Colocation in Spain/Europe is structurally cheaper than in US tier-1 markets (New York, Silicon Valley). For an own datacenter, replace it with the cost of your own space plus amortisation of the electrical and cooling infrastructure.
Capex summary per 4×H100 SXM5 node
| Line item | Range (USD) | Midpoint |
|---|---|---|
| GPUs (4× H100 SXM5) | 120,000–160,000 | 140,000 |
| Server chassis + CPU/RAM/PSU | 18,000–25,000 | 21,500 |
| InfiniBand NDR network (prorated) | 6,000–8,000 | 7,000 |
| NVMe + object storage | 4,000–9,000 | 6,500 |
| PDU/rack/other (capex) | 2,000–5,000 | 3,500 |
| Total capex per node | 150,000–207,000 | 178,500 |
Sources: GMI Cloud (Apr. 2026), Introl (Apr. 2026), Spheron (Apr. 2026), xicomputer.com (Jun. 2026).
Opex breakdown per 4×H100 SXM5 node (annual)
Energy
A 4×H100 SXM5 node at full load draws approximately:
$$P_{\text{node}} = 4 \times 700\,\text{W (TDP H100 SXM5)} + 800\,\text{W (server)} \approx 3.6\,\text{kW (IT)}$$The total datacenter power includes the cooling overhead, expressed by the PUE:
$$P_{\text{total}} = P_{\text{IT}} \times \text{PUE}$$ $$\text{annual energy cost} = P_{\text{IT}} \times \text{PUE} \times 8{,}760\,\text{h} \times \text{kWh price}$$With the base values (PUE 1.54; 0.116 €/kWh):
$$\text{energy/year} = 3.6\,\text{kW} \times 1.54 \times 8{,}760\,\text{h} \times 0.116\,\text{EUR/kWh} \approx 5{,}475\,\text{EUR}$$With a Spanish solar PPA (Q3 2025 reference price: ~34 €/MWh = 0.034 €/kWh according to PV Tech, Oct. 2025):
$$\text{energy/year (solar PPA)} = 3.6 \times 1.54 \times 8{,}760 \times 0.034 \approx 1{,}604\,\text{EUR}$$| Energy scenario | Price (€/kWh) | Energy cost/year per 4-GPU node |
|---|---|---|
| Spanish solar PPA (Q3 2025) | 0.034 | ~1,604 € |
| Industrial Spain (Sept. 2025) | 0.116 | ~5,475 € |
| European average (industrial tariff) | 0.160 | ~7,550 € |
| Worst case (no PPA, high tariff) | 0.200 | ~9,437 € |
Staff / operations
Staff cost is the most variable line item with cluster size. For a small cluster (2–8 nodes), the rule of thumb is 0.3–0.5 FTE per cluster of GPU infrastructure support (Spheron, Apr. 2026).
| Cluster size | Estimated FTE | FTE cost (€/year, Western Europe) | Cost per 4-GPU node (€/year) |
|---|---|---|---|
| 2–4 nodes | 0.3 FTE | ~120,000 | 36,000–18,000 |
| 8–16 nodes | 0.5 FTE | ~120,000 | 7,500 |
| 32+ nodes | 1–2 FTE | ~120,000 | 3,750–7,500 |
Indicative salary reference: a GPU infrastructure engineer with CUDA, InfiniBand and Kubernetes knowledge in Western Europe, ~90,000–140,000 €/year fully loaded. Introl’s figures (Apr. 2026) in USD (~275,000 USD/year for the US) reflect the North American market, which is appreciably higher.
Maintenance and support
| Line item | Annual cost (% of hardware capex) | Per 4-GPU node (midpoint) |
|---|---|---|
| Vendor maintenance / support | 5–10 % of capex | ~7,000–14,000 USD → ~6,500–13,000 € |
| GPU failure rate (~5 % annual) × replacement cost | 5 % × 4 GPUs × ~35,000 USD = ~7,000 USD expected | ~6,500 € (amortised as a provision) |
| Minor spares (cables, modules) | ~500–1,000 € | — |
Introl quotes GPU failure rates of 2–3 % per year in small clusters; Google Research documented ~9 % annualised in Meta’s 16,384-GPU H100 cluster (Introl, Apr. 2026). 5 % is used here as a conservative intermediate value.
Depreciation (for accounting purposes)
Straight-line depreciation turns capex into an annual flow comparable to the cost of committed cloud:
$$\text{annual depreciation} = \frac{\text{node capex}}{\text{amortisation years}}$$| Node capex (USD) | 3-year amortisation (USD/year) | 5-year amortisation (USD/year) |
|---|---|---|
| 150,000 (minimum) | 50,000 | 30,000 |
| 178,500 (average) | 59,500 | 35,700 |
| 207,000 (maximum) | 69,000 | 41,400 |
H100 hardware depreciates fast: secondary-market analyses put the residual value at 20–40 % of the purchase price after 3 years (Introl, Apr. 2026). The arrival of Blackwell GB200/GB300 accelerates the perceived obsolescence.
Annual opex summary per 4×H100 SXM5 node (base scenario, 8-node cluster)
| Line item | Base scenario (€/year) | Range |
|---|---|---|
| Energy (PUE 1.54; 0.116 €/kWh) | 5,475 | 1,604–9,437 |
| Staff (0.5 FTE × 8 nodes, prorated) | 7,500 | 3,750–36,000 |
| Maintenance / support / failures | 9,000 | 5,000–15,000 |
| Rack colocation (Spain, high density) | 6,000 | 3,000–15,000 |
| Total opex per node | ~28,000 | ~13,000–75,000 |
The extreme range reflects the difference between a well amortised own datacenter with a solar PPA and cheap energy (minimum opex) and tier-1 colocation with market tariffs and senior staff.
Deriving the all-in €/GPU-hour
Formula
$$\text{EUR/GPU-hour all-in} = \frac{\frac{\text{node capex}}{\text{years}} + \text{node annual opex}}{4\,\text{GPUs} \times 8{,}760\,\text{h} \times u}$$where \(u\) is the average annual utilisation (0 to 1).
See the cost-per-token identity in cost per token and per request for the connection with throughput.
€/GPU-hour table by utilisation and scenario
Average capex (178,500 USD → ~166,000 €), 3-year amortisation → 55,300 €/year.
| Utilisation | Opex/year (base, €) | Total cost/year (€) | Useful GPU-hours/year | €/GPU-hour |
|---|---|---|---|---|
| 30 % | 28,000 | 83,300 | 10,512 | 7.93 |
| 50 % | 28,000 | 83,300 | 17,520 | 4.75 |
| 70 % | 28,000 | 83,300 | 24,528 | 3.39 |
| 80 % | 28,000 | 83,300 | 28,032 | 2.97 |
| 100 % | 28,000 | 83,300 | 35,040 | 2.38 |
Low-opex scenario (solar PPA, own datacenter, large cluster): opex/year ~13,000 €.
| Utilisation | Total cost/year (€) | €/GPU-hour |
|---|---|---|
| 50 % | 68,300 | 3.90 |
| 70 % | 68,300 | 2.78 |
| 80 % | 68,300 | 2.43 |
| 100 % | 68,300 | 1.95 |
High-opex scenario (market tariff, expensive colocation, small cluster): opex/year ~75,000 €.
| Utilisation | Total cost/year (€) | €/GPU-hour |
|---|---|---|
| 50 % | 130,300 | 7.44 |
| 70 % | 130,300 | 5.31 |
| 80 % | 130,300 | 4.65 |
| 100 % | 130,300 | 3.72 |
From €/GPU-hour to €/1M tokens
The cost-per-token identity connects the hardware cost with the inference cost:
$$\text{EUR/1M tokens} = \frac{\text{EUR/GPU-hour} \times 10^6}{\text{throughput (tok/s)} \times 3{,}600}$$For reference throughputs on H100 SXM5 with vLLM (see capacity planning for on-premise inference):
| Model | Typical throughput (tok/s per GPU) | Source |
|---|---|---|
| Llama-3 70B FP8, high batch | ~2,800 | Series B benchmarks |
| Llama-3 8B FP16, medium batch | ~9,000 | Series B benchmarks |
| Mixtral 8×7B, high batch | ~4,500 | Series B benchmarks |
€/1M tokens table in the base scenario (€/GPU-hour 3.39 at 70 % utilisation):
| Model | Throughput (tok/s) | €/1M tokens |
|---|---|---|
| Llama-3 70B FP8 | 2,800 | ~0.336 |
| Llama-3 8B FP16 | 9,000 | ~0.105 |
| Mixtral 8×7B | 4,500 | ~0.209 |
At 50 % utilisation (€/GPU-hour 4.75):
| Model | €/1M tokens |
|---|---|
| Llama-3 70B FP8 | ~0.471 |
| Llama-3 8B FP16 | ~0.147 |
Occupancy (batching) multiplies the effective throughput and lowers the €/1M tokens without changing the hardware; it is analysed in GPU utilisation as a FinOps lever.
Break-even on-prem vs cloud
The break-even formula
Break-even happens when the total annual on-prem cost equals the annual cloud cost at the same utilisation:
$$\text{annual cloud cost} = \text{cloud GPU-hour price} \times 4\,\text{GPUs} \times 8{,}760\,\text{h} \times u$$ $$\text{break-even}: \quad \frac{\text{capex/year} + \text{opex/year}}{4 \times 8{,}760 \times u} = \text{cloud GPU-hour price}$$Solving for the break-even utilisation:
$$u^* = \frac{\text{capex/year} + \text{opex/year}}{4 \times 8{,}760 \times \text{cloud GPU-hour price}}$$Break-even table by cloud mode and on-prem scenario
Base on-prem scenario (capex/year 55,300 €, opex/year 28,000 €, total 83,300 €/year per 4-GPU node):
| Cloud reference (price/GPU-hour) | USD equiv. | Break-even utilisation \(u^*\) | Note |
|---|---|---|---|
| Neocloud on-demand (Lambda/Spheron ~2.90 USD) | 2.90 USD (~2.70 €) | >100 %, on-prem does not compete | Neocloud on-demand is cheaper even at full utilisation |
| Neocloud 3-year reserved (CoreWeave ~1.49–2.10 USD) | ~1.80 USD (~1.67 €) | >100 %, impossible | Neocloud reserved beats on-prem in every scenario of this model |
| AWS p5 on-demand (6.88 USD/GPU-hour) | 6.88 USD (~6.40 €) | ~47 % | Above 47 %, the average on-prem beats AWS on-demand |
| AWS p5 3-year reserved (~2.97 USD/GPU-hour) | 2.97 USD (~2.76 €) | >100 % | — |
| GCP A3 on-demand (~10.98 USD/GPU-hour) | 10.98 USD (~10.21 €) | ~29 % | Above 29 %, on-prem beats GCP on-demand |
| Azure ND H100 v5 on-demand (~12.29 USD/GPU-hour) | 12.29 USD (~11.43 €) | ~26 % | — |
Low-opex scenario (total 68,300 €/year):
| Cloud reference | Break-even utilisation |
|---|---|
| AWS p5 on-demand (6.88 USD ≈ 6.40 €) | ~38 % |
| Neocloud on-demand (2.90 USD ≈ 2.70 €) | ~91 % |
| Neocloud 3-yr reserved (1.80 USD ≈ 1.67 €) | >100 % |
High-opex scenario (total 130,300 €/year):
| Cloud reference | Break-even utilisation |
|---|---|
| AWS p5 on-demand (6.88 USD ≈ 6.40 €) | ~72 % |
| GCP A3 on-demand (~10.21 €) | ~45 % |
| Azure on-demand (~11.43 €) | ~41 % |
Reading the break-even table
- Against neoclouds (on-demand or reserved), on-prem TCO does not reach break-even in any scenario of the base model. Neocloud reserved beats on-prem even at 100 % utilisation, because its hourly price is below the all-in cost of your own hardware. This is consistent with the analysis in cloud GPU: on-demand, reserved and spot.
- Against on-demand hyperscalers (AWS, GCP, Azure), on-prem does have an achievable break-even: around 26–72 % utilisation depending on the scenario. At medium-high utilisation (>70 %), on-prem clearly beats AWS/GCP/Azure on-demand.
- The variable that moves the break-even most is opex (staff above all), not hardware capex. A well sized cluster in cheap colocation with PPA energy can lower the threshold by 20 percentage points compared with the high scenario.
- For GDPR data, the break-even against US hyperscalers is skewed: the sovereignty axis rules out US hyperscalers before cost does (see sovereign on-premise vs hyperscalers).
Sensitivity analysis
TCO vs utilisation
The all-in cost per GPU-hour varies inversely with utilisation because capex is fixed:
$$\frac{d(\text{EUR/GPU-hour})}{du} = -\frac{\text{capex/year} + \text{opex/year}}{4 \times 8{,}760 \times u^2} < 0$$Going from 50 % to 80 % utilisation cuts the €/GPU-hour by \(\frac{4.75 - 2.97}{4.75} \approx 37\,\%\) in the base scenario. That 37 % reduction requires no hardware change, only more efficient scheduling (see GPU utilisation as a FinOps lever).
| Utilisation | €/GPU-hour (base scenario) | Change vs 50 % |
|---|---|---|
| 30 % | 7.93 | +67 % |
| 50 % | 4.75 | reference |
| 70 % | 3.39 | −29 % |
| 80 % | 2.97 | −37 % |
| 100 % | 2.38 | −50 % |
TCO vs energy price
| Energy price (€/kWh) | Energy opex/year | €/GPU-hour (70 % util.) | Change vs base |
|---|---|---|---|
| 0.034 (solar PPA) | 1,604 € | 3.00 | −12 % |
| 0.116 (industrial ES, base) | 5,475 € | 3.39 | reference |
| 0.160 (European average) | 7,550 € | 3.54 | +4 % |
| 0.200 (high tariff) | 9,437 € | 3.67 | +8 % |
Energy has a moderate impact on total TCO (8–12 % variation between the extremes), because hardware capex dominates. Over a very long amortisation (5 years) with a solar PPA, however, energy drops from 6 % to 1 % of total TCO and the differential is amplified. The price of energy matters more for the carbon footprint (CSRD) than for TCO when capex is dominant.
TCO vs PUE
| PUE | Cooling overhead | Energy/year (0.116 €/kWh) | €/GPU-hour (70 % util.) |
|---|---|---|---|
| 1.15 (liquid cooling, new facilities) | +15 % | 2,166 € | 3.21 |
| 1.20 (liquid, modern datacenter) | +20 % | 2,259 € | 3.23 |
| 1.48 (facilities <5 years, Uptime 2025) | +48 % | 3,490 € | 3.33 |
| 1.54 (global average, Uptime 2025) | +54 % | 3,627 € | 3.39 |
| 1.80 (legacy colocation) | +80 % | 4,260 € | 3.47 |
The difference between PUE 1.15 (liquid) and 1.80 (legacy) is barely ~8 % of the €/GPU-hour at 70 % utilisation, because energy is only a fraction of TCO. PUE matters far more for the absolute energy cost and for CSRD reporting than for total TCO when hardware is the dominant component.
TCO vs amortisation years
| Amortisation | Capex/year (average node, USD) | €/GPU-hour (70 % util., base opex scenario) |
|---|---|---|
| 3 years | 59,500 USD (~55,300 €) | 3.39 |
| 4 years | 44,625 USD (~41,500 €) | 2.99 |
| 5 years | 35,700 USD (~33,200 €) | 2.72 |
Stretching the amortisation from 3 to 5 years lowers the €/GPU-hour by ~20 %, assuming the hardware remains competitive and the resale market supports the residual value. With the refresh cycle accelerated by Blackwell GB200/GB300, a 5-year amortisation carries a higher risk of technological obsolescence.
Sensitivity heat map (€/GPU-hour at 70 % utilisation, base scenario)
| PUE 1.15 | PUE 1.54 | PUE 1.80 | |
|---|---|---|---|
| 3-yr amort., solar PPA (0.034 €) | 2.72 | 2.74 | 2.76 |
| 3-yr amort., industrial (0.116 €) | 3.21 | 3.39 | 3.47 |
| 5-yr amort., industrial (0.116 €) | 2.54 | 2.72 | 2.80 |
| 3-yr amort., high tariff (0.200 €) | 3.44 | 3.67 | 3.78 |
Decision table: cost/control/sovereignty Pareto
The table below crosses the four dimensions with no implicit hierarchy; the ordinal reading depends on each organisation’s constraints.
| Option | €/GPU-hour | Initial capex | Full stack control | EU sovereignty | Elasticity | Operational risk |
|---|---|---|---|---|---|---|
| On-prem (util. >70 %, low opex) | 2.40–3.00 | high (150–207k USD/node) | total | total | none | hardware failure, idle |
| On-prem (util. <50 %, base opex) | 4.75–7.93 | high | total | total | none | capex with no return |
| Neocloud 3-year reserved (CoreWeave, Lambda) | 1.49–2.10 USD | none | partial (API) | depends on the provider | rigid contract | minimal interruption |
| Neocloud on-demand (Lambda, Spheron) | 2.49–3.44 USD | none | partial | depends | total | no interruption |
| AWS p5 on-demand | 6.88 USD | none | minimal | NO (CLOUD Act) | total | no interruption |
| AWS p5 3-year reserved | ~2.97 USD | financial commitment | minimal | NO (CLOUD Act) | rigid | no interruption |
| Sovereign EU cloud (Scaleway, Nebius EU) | 2.15–3.85 USD | none | partial | yes (EU) | total | no interruption |
| Hybrid on-prem base + EU cloud peak | 2.00–3.50 (weighted) | medium | high | yes (EU) | elastic peak | operational complexity |
“EU sovereignty” column: US hyperscalers (AWS, GCP, Azure) are subject to the US CLOUD Act regardless of the datacenter region. Nebius has a Dutch legal entity; CoreWeave is a US company. See the full analysis in sovereign on-premise vs hyperscalers.
“Full stack control” column: on-prem lets you choose the driver version, the kernel, the NCCL configuration, MIG partitioning, and any system parameter. Cloud options offer control at container/pod level, with the hypervisor and firmware opaque.
The cost/sovereignty Pareto frontier for GDPR data excludes US hyperscalers, leaving: on-prem, sovereign EU cloud, and the hybrid. Among those three, the deciding variable is sustained utilisation and the predictability of traffic (see capacity planning for on-premise LLM inference).
Integration with the series FinOps model
The all-in €/GPU-hour of on-prem is the number that feeds the cost allocation pipeline of the series:
- Cost-per-token identity (cost per token and per request): engine throughput × €/GPU-hour → €/1M tokens.
- Chargeback and showback (chargeback and showback in GPU multitenancy): the all-in €/GPU-hour is the internal price charged to each tenant of the multi-tenant cluster.
- Utilisation as a lever (GPU utilisation as FinOps): raising utilisation from 50 % to 80 % cuts the €/GPU-hour by 37 % without changing the hardware, the highest ROI in on-prem FinOps.
- Capacity planning (capacity planning for on-premise LLM inference): the number of nodes to buy depends on the percentile of base load you want to cover in iron.
- Cloud comparison (cloud GPU: on-demand, reserved and spot): the all-in €/GPU-hour goes head to head with the cloud price in table A7 to compute the break-even.
Sources
- Spheron · LLM Inference On-Premise vs GPU Cloud: 2026 Cost and Break-Even Analysis (abr. 2026) — https://www.spheron.network/blog/llm-inference-on-premise-vs-cloud/
- Introl · GPU Infrastructure TCO Model: 5-Year Cost Analysis for Enterprise AI (abr. 2026) — https://introl.com/blog/gpu-infrastructure-tco-5-year-cost-model
- GMI Cloud · NVIDIA H100 GPU Pricing: 2026 Rent vs. Buy Cost Analysis (abr. 2026) — https://www.gmicloud.ai/en/blog/nvidia-h100-gpu-pricing-2026-rent-vs-buy-cost-analysis
- Uptime Institute · Global Data Center Survey 2025 (PDF oficial) — https://datacenter.uptimeinstitute.com/rs/711-RIA-145/images/2025.Annual.Survey.Report.pdf
- Uptime Institute · Global Data Center PUE Stalls at 1.54 (comunicado, oct. 2025) — https://mgrid.org/2025/10/01/uptime-institute-data-center-pue-stagnation-2025-liquid-cooling/
- GlobalPetrolPrices · Spain Business Electricity Price (sept. 2025) — https://www.globalpetrolprices.com/Spain/electricity_prices/
- PV Tech · European Solar PPA Prices Fall Below 35 €/MWh in Q3 2025 (oct. 2025) — https://www.pv-tech.org/european-solar-ppa-prices-fall-below-35-mwh-q3-2025/
- Xi Computers · Supermicro SYS-821GE-TNHR 8U GPU Server Pricing (jun. 2026) — https://www.xicomputer.com/Solutions/Data-Center-Servers/SYS-821GE-TNHR.asp
- Encoradvisors · Data Center Colocation Pricing 2026 — https://encoradvisors.com/data-center-colocation-pricing/
- CloudZero · H100 GPU Cost In 2026: Buy, Rent, and Cloud Pricing Compared — https://www.cloudzero.com/blog/h100-gpu-cost/
- Spheron · AI Inference Power Consumption and GPU Electricity Costs: 2026 Guide — https://www.spheron.network/blog/ai-inference-power-electricity-cost-2026/
- NVIDIA · DGX SuperPOD H100 Electrical Specifications (documentación oficial) — https://docs.nvidia.com/dgx-superpod/design-guides/dgx-superpod-data-center-design-h100/latest/electrical.html